This invention relates generally to simulation modeling and, more specifically, to computational methods for simulating and predicting the activity of biochemical and biological network models.
Therapeutic agents, including drugs and gene-based agents, are being rapidly developed by the pharmaceutical industry with the goal of preventing or treating human disease. Dietary supplements, including herbal products, vitamins and amino acids, are also being developed and marketed by the nutraceutical industry. Because of the complexity of biochemical reaction networks, even relatively minor perturbations caused by a therapeutic agent or a dietary component on the abundance or activity of a particular target, such as a metabolite, gene or protein, can affect hundreds of biochemical reactions. These perturbations can lead to desirable therapeutic effects, such as cell stasis or cell death in the case of cancer cells or other pathologically hyperproliferative cells. However, these perturbations can also lead to undesirable side effects, such as production of toxic byproducts.
Traditionally the identification of drugs and nutraceuticals has relied upon early stage screening and testing in which the effects of candidate drugs on individual genes or gene products are observed. This approach, although helpful for identifying a particular gene or gene product as a target for a particular disease, is often incapable of identifying the effects that the candidate drug or the drug inhibited target will have on other molecular components of the cell or organism. It is often not until late stage testing with human subjects that unwanted or even dangerous side effects are observed. Failure to select a candidate drug in early stage testing that is without side effects can result in harm to individuals participating in clinical trials and significant delays in curing individuals suffering from disease due to pursuing the wrong drug.
In order to design effective methods of repairing, engineering or disabling cellular activities, it is essential to understand cellular behavior from an integrated perspective. Methods have recently been developed to reconstruct biological reaction networks that occur within organisms, with the goal of being able to model them and then use simulation to predict and analyze organismal behavior. One of the most powerful current approaches to modeling complex biological reaction networks involves constraints-based modeling. This approach provides a mathematically defined solution space wherein all possible behaviors of the reconstructed biological reaction network must lie. The solution space can then be explored to determine the range of capabilities and preferred behavior of the biological system under various conditions.
A combination of many high throughput technologies is now providing information on a scale that includes entire genomes, the complete set of gene products encoded by the genomes, and molecular functions that occur in a cell or organism. The ability to create genome scale constraints-based models requires that vast amounts of biological information be assimilated. Although genome scale models have been produced for a variety of organisms and have been shown to accurately predict a number of cell functions, it is currently difficult and time consuming to build new models and many organisms for which genome scale information is available currently lack genome scale models. Furthermore, it is currently difficult to view the content of models and to cross-reference the information in the models with the information available in biological databases and with other models. Thus, for many models, errors go unnoticed or are difficult to correct once the model is built.
Thus, there exists a need for constraints-based models for the increasing number and variety of organisms for which genomes are being sequenced. A need also exists for methods to efficiently build and modify existing constraints-based models. The present invention satisfies these needs and provides related advantages as well.